

def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				Union[str, Any]:
 '''simple docstring'''


 if not isinstance(a__		,				a__						):
  raise TypeError('Input value must be an \'int\' type'						)

 SCREAMING_SNAKE_CASE		    =      0
 while number:
  position += 1
  number >>= 1

 return position


if __name__ == "__main__":
      import doctest

      doctest.testmod()




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			with open(a__) as metadata_file:
						a_						:						Any 							= json.load(a__)
			a_						:						Dict 							= LukeConfig(use_entity_aware_attention=a__  ,	**metadata["""model_config"""])

			# Load in the weights from the checkpoint_path
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")

			# Load the entity vocab file
			a_						:						List[str] 							= load_entity_vocab(a__)

			a_						:						int 							= RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""])

			# Add special tokens to the token vocabulary for downstream tasks
			a_						:						Optional[Any] 							= AddedToken("""<ent>"""  ,	lstrip=a__  ,	rstrip=a__)
			a_						:						int 							= AddedToken("""<ent2>"""  ,	lstrip=a__  ,	rstrip=a__)
			tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]})
			config.vocab_size += 2

			print(f'''Saving tokenizer to {pytorch_dump_folder_path}''')
			tokenizer.save_pretrained(a__)
			with open(os.path.join(a__  ,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""])  ,	"""w""") as f:
						json.dump(a__  ,	a__)

			a_						:						List[str] 							= LukeTokenizer.from_pretrained(a__)

			# Initialize the embeddings of the special tokens
			a_						:						Optional[int] 							= state_dict["""embeddings.word_embeddings.weight"""]
			a_						:						List[str] 							= word_emb[tokenizer.convert_tokens_to_ids(["""@"""])[0]].unsqueeze(0)
			a_						:						List[Any] 							= word_emb[tokenizer.convert_tokens_to_ids(["""#"""])[0]].unsqueeze(0)
			a_						:						Optional[Any] 							= torch.cat([word_emb, ent_emb, enta_emb])

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers):
						for matrix_name in ["query.weight", "query.bias"]:
									a_						:						Any 							= f'''encoder.layer.{layer_index}.attention.self.'''
									a_						:						List[str] 							= state_dict[prefix + matrix_name]
									a_						:						List[Any] 							= state_dict[prefix + matrix_name]
									a_						:						Dict 							= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			a_						:						str 							= state_dict["""entity_embeddings.entity_embeddings.weight"""]
			a_						:						int 							= entity_emb[entity_vocab["""[MASK]"""]]

			a_						:						int 							= LukeModel(config=a__).eval()

			a_							,					a_						:						Optional[int] 							= model.load_state_dict(a__  ,	strict=a__)
			if not (len(a__) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f'''Missing keys {", ".join(a__)}. Expected only missing embeddings.position_ids''')
			if not (all(key.startswith("""entity_predictions""") or key.startswith("""lm_head""") for key in unexpected_keys)):
						raise ValueError(
						    """Unexpected keys"""
						    f''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions") or key.startswith("lm_head"))])}''')

			# Check outputs
			a_						:						List[Any] 							= LukeTokenizer.from_pretrained(a__  ,	task="""entity_classification""")

			a_						:						str 							= (
			    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
			    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
			)
			a_						:						str 							= (3_9, 4_2)
			a_						:						Tuple 							= tokenizer(a__  ,	entity_spans=[span]  ,	add_prefix_space=a__  ,	return_tensors="""pt""")

			a_						:						List[str] 							= model(**a__)

			# Verify word hidden states
			if model_size == "large":
						a_						:						Optional[int] 							= torch.Size((1, 4_2, 1_0_2_4))
						a_						:						List[str] 							= torch.tensor(
						    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
			else:  # base
						a_						:						List[str] 							= torch.Size((1, 4_2, 7_6_8))
						a_						:						str 							= torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''')
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						a_						:						Dict 							= torch.Size((1, 1, 1_0_2_4))
						a_						:						int 							= torch.tensor([[0.0466, -0.0106, -0.0179]])
			else:  # base
						a_						:						Optional[Any] 							= torch.Size((1, 1, 7_6_8))
						a_						:						str 							= torch.tensor([[0.1457, 0.1044, 0.0174]])

			if not (outputs.entity_last_hidden_state.shape != expected_shape):
						raise ValueError(
						    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
						    f''' {expected_shape}''')
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print("""Saving PyTorch model to {}""".format(a__))
			model.save_pretrained(a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[str] 							= {}
			with open(a__  ,	"""r"""  ,	encoding="""utf-8""") as f:
						for index, line in enumerate(a__):
									a_							,					a_						:						List[Any] 							= line.rstrip().split("""\t""")
									a_						:						Any 							= index

			return entity_vocab


if __name__ == "__main__":
	__snake_case				:      Tuple   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
	parser.add_argument(
	    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
	)
	parser.add_argument(
	    """--entity_vocab_path""",
	    default=None,
	    type=str,
	    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
	)
	parser.add_argument(
	    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
	)
	__snake_case				:      List[str]   		=			parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)


